AI Feedback Design Principles
Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning...
Install
npx skills add GarethManning/education-agent-skills --skill ai-feedback-design-principles -g -a claude-code -y- Repository
- GarethManning/education-agent-skills939
- Category
- Engineering
- Updated
- 13d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.
AI Feedback Design Principles
What This Skill Does
Evaluates a proposed AI feedback design against research criteria for effective automated feedback and suggests specific improvements. This skill takes a feedback scenario (what the student did) and the current or proposed AI response (what the system says), then analyses the feedback against principles from Shute (2008), Narciss (2008), Hattie & Timperley (2007), and emerging LLM feedback research (Dai et al., 2023). The output includes a diagnosis of what's working and what isn't, a redesigned version of the feedback, and practical implementation guidance. The core challenge is that most AI feedback falls into one of two failure modes: it's either too vague to be actionable ("Good effort! Try to improve your argument.") or too specific and does the thinking for the student ("Your thesis should be: Climate change is the defining challenge of our generation because…"). Effective feedback lives in the narrow space between these extremes, specific enough that the student knows what to do, but not so specific that it bypasses the cognitive work that produces learning. AI is specifically valuable here because it can generate feedback at scale, but this makes the design principles even more critical: bad feedback at scale is worse than no feedback at all.
